融合梯度信息的改进引导滤波

目的 为了有效消除引导滤波平滑图像后产生的光晕现象，提出一种新型的融合梯度信息的改进引导滤波算法。方法 该算法借助引导图像的梯度信息来判断图像边缘位置，并结合指数函数框架设计权值来控制不同图像区域内的平滑倍数，使改进后的引导滤波能够自适应地区分和强调边缘，从而避免边缘附近由于过度模糊所引入的光晕现象。结果 与引导滤波算法相比，本文算法能在保边平滑的同时较好地抑制光晕，并在结构相似性（SSIM）评价和峰值信噪比（PSNR）评价中分别取得最高约30%和15%左右的质量提升。结论 本文算法具有较好的鲁棒性，在图像平滑、图像细节增强、多曝光图像融合等多种图像处理相关应用中均有着良好的表现。

Improved guided image filtering integrated with gradient information

Objective To eliminate halo artifacts produced by guided image filtering, this paper proposes a novel, improved, guided image filter that is integrated with gradient information. Method Our method uses gradient information of guidance images to determine edge positions, and designs the weight model based on an exponential function to control the smoothness of different image regions. It makes an improved guided image filter that adaptively finds edges and emphasizes them, thereby avoiding the halo introduced by excessive smooth near edges. Result Experiments show that our method can avoid halo artifacts during periods of edge-preserving smoothing, and respectively attains approximately 30% and 15% higher advancement for SSIM and PSNR, compared with the guided image filter. Conclusion Our method has greater robustness and performance in many computer vision and computer graphics applications such as image smoothing, detail enhancement, and multi-exposure fusion.